Ubiquitous Assessment of the Recovery of Cancer Patients Using Consumer-Level Activity Trackers

Performance Status (PS) variability is a powerful tool to evaluate overall condition, treatment needs and survival chances of cancer patients. Traditionally, its assessment has relied on the experience of oncologists when interpreting results of clinical tests and when interviewing the patients. Meanwhile, consumer-level activity trackers have obtained good results in behavior-change oriented intervention trials and Fitbit devices have demonstrated enough reliability to provide objective data related to physical activity, but the clinical possibilities of the data collected has been neglected. This work presents a system design for ubiquitous assessment of PS by means of objective and quantifiable data from different sources: medical history, self-reported quality-of-life questionnaires and a commercial activity tracker Fitbit Alta HR. The system proposed aims to contextualize and model the recovery process of breast cancer patients during chemotherapy treatment.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) Projects TIN2015-71873-R and TIN2015-67020-P together with the European Fund for Regional Development (FEDER). This work has also been partially supported by the FPU Spanish Grant FPU16/04201

Surg Oncol

Objectives:Pre-operative exercise may improve functional outcomes for lung cancer patients, but barriers associated with cost, resources, and burden make it challenging to deliver pre-operative exercise programs. The goal of this proof-of-concept study was to determine level of moderate-vigorous physical activity (MVPA) and change in aerobic capacity after participation in a pre-operative exercise intervention.Materials and Methods:Eighteen patients scheduled for surgery for suspected stage I-III lung cancer received an exercise prescription from their surgeon and wore a commercially-available device that tracked their daily MVPA throughout the pre-operative period. Descriptive statistics were used to calculate adherence to the exercise prescription. A one-sample t test was used to explore change in aerobic capacity from baseline to the day of surgery.Results:Participants exhibited a mean of 20.4 (sd = 46.2) minutes of MVPA per day during the pre-operative period. On average, the sample met the goal of 30 minutes of MVPA on 16.4% of the days during the pre-operative period. The mean distance achieved at baseline for the six-minute walk test was 456.7 meters (sd = 72.9), which increased to 471.1 meters (sd = 88.4) on the day of surgery. This equates to a mean improvement of 13.8 meters (sd=37.0), but this difference was not statistically different from zero (p = 0.14). Eight of the 17 participants (47%) demonstrated a clinically significant improvement of 14 meters or more.Conclusion:A surgeon-delivered exercise prescription plus an activity tracker may promote clinically significant improvement in aerobic capacity and MVPA engagement among patients with lung cancer during the pre-operative period, but may need to be augmented with more contact with and support from practitioners over time to maximize benefits.T32 MH073553/MH/NIMH NIH HHSUnited States/UL1 TR001086/TR/NCATS NIH HHSUnited States/U48 DP005018/DP/NCCDPHP CDC HHSUnited States/U48DP005018/ACL/ACL HHSUnited States/K23 AG051681/AG/NIA NIH HHSUnited States/2022-06-01T00:00:00Z33813267PMC821719711425vault:3720

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

a critical review

The availability of wearable devices (WDs) to collect biometric information and their use during activities of daily living is significantly increasing in the general population. These small electronic devices, which record fitness and health-related outcomes, have been broadly utilized in industries such as medicine, healthcare, and fitness. Since they are simple to use and progressively cheaper, they have also been used for numerous research purposes. However, despite their increasing popularity, most of these WDs do not accurately measure the proclaimed outcomes. In fact, research is equivocal about whether they are valid and reliable methods to specifically evaluate physical activity and health-related outcomes in older adults, since they are mostly designed and produced considering younger subjects? physical and mental characteristics. Additionally, their constant evolution through continuous upgrades and redesigned versions, suggests the need for constant up-to-date reviews and research. Accordingly, this article aims to scrutinize the state-of-the-art scientific evidence about the usefulness of WDs, specifically on older adults, to monitor physical activity and health-related outcomes. This critical review not only aims to inform older consumers but also aid researchers in study design when selecting physical activity and healthcare monitoring devices for elderly people.DB19-D819-F720 | Carlos Eduardo da Silva TeixeiraN/

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Development and implementation of a remote monitoring and coaching intervention delivered using digital health technology for people with a history of cancer.

There is a need to improve care practices to optimally enhance physical health and health- related quality of life in people with a history of cancer. Intensive treatment of cancer can impact patients both acutely and chronically as long-term or late effects well after treatment completion. As a result, both patients with cancer and cancer survivors need additional support Supportive cancer care, including survivorship and rehabilitation services focuses on developing strategies to support survivors’ well-being and recovery during and after cancer treatment. However, despite the evidence-based benefits of these services, many barriers still exist that may restrict patients with cancer from participation and engagement. One possible solution to these challenges is the use of digital health technologies. The aim of this research was to explore current gaps in knowledge regarding digital health enabled supportive cancer care and design and develop a digital health enabled intervention, specifically tailored to the needs of people with a cancer diagnosis. The experience culminated in the implementation of a 10-week prospective cohort trial, focused on the feasibility and acceptability of a patient-provider tracking and exercise coaching portal. As evidenced by the research studies presented within this thesis, findings suggest that patient-centric supportive care can be provided to cancer patients using a digital health enabled approach. Further, remote monitoring and individual exercise coaching can feasibly be offered to patient populations who may not be able to conveniently access support services, or who choose to access these services remotely. Several recommendations for future research and future directions were provided to further this area of research

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinic

PUSHING THE BOUNDARIES OF CONSUMER GRADE WEARABLE DEVICES IN HEALTH CARE FOR OLDER ADULTS

Background: The proliferation of wearable and mobile devices in recent years has led to the generation of unprecedented amounts of health-related data. Together with the growing population of older adults in Canada, the increasing adoption of these technologies created a momentous opportunity to improve the way we deliver, access, and interact with the health care system. Many have recognized the opportunity, yet there is a lack of evidence on how these devices and the growing size of health data can be used to transform health care and benefit us. In Chapter 2, a review of the literature was presented to identify the current evidence of wearable technology and gaps that exist in aging research. Based on the literature review, one promising way to use wearable devices is to assess frailty, which can contribute to improving care and enhancing aging-in-place. Chapter 3 summarizes key concepts related to wearable devices including mobile health, patient-generated health data, big data, predictive algorithms, machine learning, and artificial intelligence. While in-depth mathematical representation of these big data analytics is outside the scope of this dissertation, this chapter provides foundational information along with examples found in health care settings. Objective: The overall aim of this dissertation was to investigate possible use of consumer-grade wearable devices and the patient-generated health data to improve the health of older adults. Methods: This thesis is presented as three individual studies included in Chapters 4 to 6. Study 1 aimed to investigate use of wearable devices to predict and find associations with frailty for community-dwelling older adults receiving home care service. Participants were asked to wear wearable device for 8 days in their home environment and no supervision was provided. Frailty level was assessed using the Fried Frailty Index. Other variables were collected including Charlson Comorbidity Index, independence using the Katz Index, and home care service utilization level. A sequential stepwise feature selection method was used to determine variables that are fitted in multiple variable logistic regression model to predict frailty. Study 2 extended the investigation of possible use of wearable devices for understanding frailty by examining the relationship between wearable device data and frailty progression among critical illness survivors from an intensive care unit at Kingston General Hospital. Participants were assessed for frailty using the Clinical Frailty Scale three times; at admission, at hospital discharge, and at 4-weeks post-hospital discharge. The changes in frailty level between the three time points were used to identify association with wearable device data that was collected for 4 weeks post-hospital discharge. Demonstrating evidence for wearable devices and patient-generated health data in research does not guarantee its use in real life. In Study 3, a mixed method study was conducted to explore clinicians’ and older adults’ perceptions of patient-generated health data. Focus group interviews were conducted with older adults and health care providers from the Greater Toronto Area and the Kitchener-Waterloo region. A questionnaire that aimed to explore perceived usefulness of a range of different patient-generated health data was embedded in the study design. Focus group interviews were transcribed verbatim. Line by line coding was conducted on all interviews followed by thematic analysis. Results: Results from Study 1 indicate data generated from wearable devices are closely linked to frailty level. Results showed a significant difference between frail and non-frail participants in age (p<0.01), home care service utilization (p=0.012), daily step count (p=0.04), total sleep time (p=0.010), and deep sleep time (p<0.01). Total sleep time (r=0.41, p=0.012) and deep sleep time (r=0.53, p<0.01) were associated with frailty level. A receiver operating characteristics area under the curve of 0.90 was achieved using deep sleep time, sleep quality, age, and education level (Hosmer-Lemeshow p=0.88), demonstrating that data from wearable devices can augment the demographic and conventional clinical data in predicting frailty status. Results from Study 2 demonstrated that frailty level increases significantly following a critical illness (p=0.02). Frail survivors had significantly lower daily step counts (p=0.02). Daily step count (r=-0.72, p=0.04) and mean heart rate (r=-0.72, p=0.046) were strongly correlated with frailty level at admission and discharge. Mean standard deviation of heart rate was correlated with the change in frailty status from admission to 4-week follow-up (r=0.78, p<0.05). The results demonstrated a relationship between the worsening of frailty due to critical illness and the pattern of increasing step count (r=0.65, p=0.03) and heart rate (r=0.62, p=0.03) over the 4-week observation period. Results from Study 3 provided an understanding of what older adults and clinicians considered barriers to using patient-generated health data in their care and clinical settings. Four main themes were identified from the focus group interviews: influence of patient-generated health data on patient-provider trust; reliability of patient-generated health data; meaningful use of patient-generated health data and decision support system; and perceived clinical benefits and intrusiveness of patient-generated health data. Results from the questionnaire and focus group interviews demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing their health and delivering high quality care. Discussion: This dissertation provides evidence for using consumer-grade wearable device to assess, monitor, and predict frailty for older adults who receive home care or survived critical illness. The possibility of using a wearable device to assess frailty can enable health care providers to obtain frailty information in a timely manner, which is challenging to acquire otherwise due to a lack of appropriate tools in primary care, ambulatory care, home and community care, critical illness care, and other sectors. There was a distinct relationship between failure to recover frailty level from critical illness and the pattern of daily step count and heart rate. This can enable early detection of critical illness survivors who may not return to pre-critical illness level. It can provide guidance to identify those who may benefit the most from follow-up visits and elevated treatment. To ensure the benefits of patient-generated health data are realized, it must be integrated into health care. There are technical challenges that prevent such integration and discussion around policies and regulations must begin to make progress. Conclusion: This dissertation demonstrated use of wearable devices to assess frailty and identified factors that can hinder the integration of patient-generated health data into health care. It opened a possibility of assessing frailty, expanding the boundaries of current use of consumer-grade wearable devices